Technical Field
The present disclosure relates to machine-readable symbol readers and, more particularly, aiming systems and methods for machine-readable symbol readers.
Description of the Related Art
Machine-readable symbols encode information in a form that can be optically read via a machine-readable symbol reader or scanner. Machine-readable symbols take a variety of forms, the most commonly recognized form being the linear or one-dimension barcode symbol. Other forms include two-dimensional machine-readable symbols such as stacked code symbols, and area or matrix code symbols. These machine-readable symbols are typically composed on patterns of high and low reflectance areas. For instance, a barcode symbol may comprise a pattern of black bars on a white background. Also for instance, a two-dimensional symbol may comprise a pattern of black marks (e.g., bars, squares or hexagons) on a white background. Machine-readable symbols are not limited to being black and white, but may comprise two other colors, and/or may include more than two colors (e.g., more than black and white).
Machine-readable symbols are typically composed of elements (e.g., symbol characters) which are selected from a particular machine-readable symbology. Information is encoded in the particular sequence of shapes (e.g., bars) and spaces which may have varying dimensions. The machine-readable symbology provides a mapping between machine-readable symbols or symbol characters and human-readable symbols (e.g., alpha, numeric, punctuation, commands). A large number of symbologies have been developed and are in use, for example Universal Product Code (UPC), European Article Number (EAN), Code 39, Code 128, Data Matrix, PDF417, etc.
Machine-readable symbols have widespread and varied applications. For example, machine-readable symbols can be used to identify a class of objects (e.g., merchandise) or unique items (e.g., patents). As a result, machine-readable symbols are found on a wide variety of objects, such as retail goods, company assets, and documents, and help track production at manufacturing facilities and inventory at stores (e.g., by scanning items as they arrive and as they are sold). In addition, machine-readable symbols may appear on a display of a portable electronic device, such as a mobile telephone, personal digital assistant, tablet computer, laptop computer, or other device having an electronic display. For example, a customer, such as a shopper, airline passenger, or person attending a sporting event or theater event, may cause a machine-readable symbol to be displayed on their portable electronic device so that an employee (e.g., merchant-employee) can read the machine-readable symbol via a data reader to allow the customer to redeem a coupon or to verify that the customer has purchased a ticket for the event.
Machine-readable symbol readers or data readers are used to capture images or representations of machine-readable symbols appearing on various surfaces to read the information encoded in the machine-readable symbol. One type of commonly used machine-readable symbol reader is an imager- or imaging-based machine-readable symbol reader. Imaging-based machine-readable symbol readers typically employ flood illumination to simultaneously illuminate the entire machine-readable symbol, either from dedicated light sources, or in some instances using ambient light. Another type of machine-readable symbol reader is a scanning or laser-based (i.e., flying spot) machine-readable symbol reader, which scans a relative narrow beam or spot of light sequentially across the machine-readable symbol.
Imaging-based machine-readable symbol readers typically include solid-state image circuitry, such as charge-coupled devices (CCDs) or complementary metal-oxide semiconductor (CMOS) devices, and may be implemented using a one-dimensional or two-dimensional imaging array of photosensors (or pixels) to capture an image of the machine-readable symbol. One-dimensional CCD or CMOS readers capture a linear cross-section of the machine-readable symbol, producing an analog waveform whose amplitude represents the relative darkness and lightness of the machine-readable symbol. Two-dimensional CCD or CMOS readers may capture an entire two-dimensional image. The image is then processed to find and decode a machine-readable symbol. For example, virtual scan line techniques for digitally processing an image containing a machine-readable symbol sample across an image along a plurality of lines, typically spaced apart and at various angles, somewhat like a scan pattern of a laser beam in a scanning or laser-based scanner.
Reading a symbol typically employs generating an electrical signal having an amplitude determined by the intensity of the collected light. Relatively less reflective or darker regions (e.g., bars or other marks) may, for example, be characterized or represented in the electrical signal by an amplitude below a threshold amplitude, while relatively more reflective or lighter regions (e.g., white spaces) may be characterized or represented in the electrical signal an amplitude above the threshold amplitude. When the machine-readable symbol is imaged, positive-going and negative-going transitions in the electrical signal occur, signifying transitions between darker regions and lighter regions. Techniques may be used for detecting edges of darker regions and lighter regions by detecting the transitions of the electrical signal. Techniques may also be used to determine the dimensions (e.g., width) of darker regions and lighter regions based on the relative location of the detected edges and decoding the information represented by the machine-readable symbol.
In machine-readable symbol readers, a return light signal from the object or symbol being read is focused onto a sensor or sensor array. In the example of a machine-readable symbol reader reading marks and spaces of a typical machine-readable symbol, there needs to be sufficient difference in signal intensity between the signal corresponding to the light space and the signal corresponding to the dark bar in order for the processor to differentiate therebetween. Depth of field plays an important role in effectively detecting an image at the sensor. Thus, in machine-readable symbol reading applications there has been a demand for accurately reading the machine-readable symbols over the entire depth of field, i.e., the range of distance over which the machine-readable symbol reader can effectively scan.
Machine-readable symbol readers may be fixed, for example at a point of sale, or may be handheld or even mobile. Whether fixed, handheld, or mobile, the machine-readable symbol to be read must be within a field of view of the machine-readable symbol reader. Thus, some machine-readable symbol readers include an aiming system which provides or projects an aiming pattern. This allows a user to position the machine-readable symbol reader (e.g., handheld) relative to a target or machine-readable symbol, or conversely position the target or machine-readable symbol relative to the machine-readable symbol reader (e.g., fixed).
Conventional machine-readable symbol readers, however, have proven to be problematic. For instance, FIGS. 1A and 1B illustrate a typical machine-readable symbol reader 1. The machine-readable symbol reader 1 includes an illumination source 2 and an image sensor or sensor array 4. The illumination source 2 emits light to generate an aiming beam 6 which impinges on an item or object 8 positioned within a field of view 10 to generate an aiming pattern. An image of the item or object 8 is captured by the image sensor or sensor array 4. The returned image can be directed onto the image sensor or array 4 along an optical axis 14, which extends from the image sensor or array 4.
As illustrated in FIGS. 1A and 1B, the illumination source 2 is angularly spaced with respect to the optical axis 14. Consequently, a spot 15 projected on the item or object 8 by the angular orientation of the illumination source 2 aligns with the optical axis 14 at a singular point 16 across the depth of field. Having a singular alignment point can compromise the accuracy of the reading capability of the image sensor or array 4 to read the image of the item or object 8 to be returned to the image sensor or array 4. For instance, a user may laterally move the machine-readable symbol reader 1 to align the spot 15 with a center of the item or object 8. As a result, the optical axis 14 may no longer be aligned with the center of the item or object 8. In other instances, the user may have to move the machine-readable symbol reader 1 in the forward-aft direction within the field of view 10 to align the spot 15 with the center of the item or object 8.
Solutions addressing the deficiencies in the alignment capabilities of conventional machine-readable symbol readers have involved using at least two illumination sources generating parallel aiming beams to project aiming patterns that encompass the center of the item or object. However, such solutions increase manufacturing and labor costs and complexity of the machine-readable symbol readers.